Case studyBuilding an AI platform that helps Amazon brands grow organically and optimize ad performance
DeepM combines Amazon SP-API, Advertising API and Brand Analytics data to lift organic rankings and cut wasted ad spend.
- Organic keyword ranking optimization
- Advertising performance intelligence
- AI-powered listing optimization
AI-powered Amazon SEO and advertising intelligence.
DeepM continuously analyzes marketplace activity, advertising campaigns, keyword performance and competitor movements, then recommends the actions that improve both organic visibility and paid efficiency — tracking how every change affects rankings and sales.
- Which search terms generate the highest organic sales?
- Which keywords deserve more advertising budget?
- Which campaigns have high ACOS or TACOS?
- Where is advertising spend being wasted?
- Why is a competitor ranking above us organically?
- Which products deserve additional ad spend?
- Which keywords should move from PPC to organic targeting?
- Which listing improvements increase conversion and ranking?
Amazon generates enormous amounts of marketplace and advertising data.
Every product produces data across SP-API, the Advertising API, Brand Analytics, Search Query Performance, campaign reports and the catalogue. Most sellers have that data — turning billions of records into profitable decisions is the part that takes engineering and AI.
- Billions of marketplace and advertising records
- Multiple Amazon APIs with different schemas
- Search Term, Campaign and Brand Analytics reports
- Organic and Sponsored rankings changing daily
- Campaign performance across thousands of keywords
- Decisions needed in seconds instead of days
Advertising budgets were wasted
Campaigns consumed thousands in ad spend while high ACOS and TACOS ate the margin, with little visibility into where the budget should be reallocated.
Organic rankings grew slowly
Teams leaned on Sponsored Products because they couldn't identify which keywords had the highest organic ranking potential.
Campaign optimization took days
Advertising reports had to be exported by hand before anyone could analyse impressions, CTR, CPC, ROAS, conversions and keyword performance.
Too much data, too little insight
SP-API, Advertising API, Brand Analytics, Search Query Performance and keyword reports all lived separately, so nobody saw the complete picture.
Listing optimization lacked evidence
Titles, bullets, backend keywords and descriptions were updated on assumption rather than on real keyword performance and conversion data.
Organic and paid were disconnected
Teams couldn't see how campaigns influenced organic rank, or which keywords should transition from paid acquisition to organic growth.
From Amazon APIs to AI recommendations.
Six stages, running continuously — most of it happens before anyone opens the platform.
Amazon SP-API, Advertising API and Brand Analytics
- 01
Collect
Continuously ingests marketplace intelligence from Amazon SP-API, the Advertising API, Brand Analytics, Search Query Performance, campaign reports, keyword reports and catalogue data.
- 02
Process
Cleans, normalises, validates and reconciles millions of marketplace and advertising records into one consistent analytical model.
- 03
Store
Loads billions of records into Google BigQuery for large-scale historical analysis across products, keywords, campaigns and marketplaces.
- 04
Analyse
AI evaluates performance across organic and paid to find the biggest growth opportunities.
- 05
Recommend
Turns that analysis into specific, prioritised actions a team can apply this week.
- 06
Measure
Tracks what each optimization actually changed, so teams know which moves improved performance.
Recommendations, and proof they worked
From guessing to knowing.
Before
- Guessing advertising budgets
- High ACOS with little explanation
- Rising TACOS every month
- Manual campaign optimization
- Organic rankings improved slowly
- Separate SP-API and Advertising reports
- No understanding of keyword profitability
- Listing changes based on intuition
After
- AI-driven ad spend optimization
- Lower ACOS and healthier TACOS
- Better ROAS across campaigns
- Faster campaign optimization
- Higher organic keyword rankings
- Unified SP-API and Advertising intelligence
- Profitable keyword prioritization
- Listing improvements backed by marketplace data
The stack behind it.
- Cloud
- Google Cloud
- Pub/Sub
- Cloud Run
- BigQuery
- Cloud SQL
- Cloud Storage
- Backend
- Java
- Spring Boot
- Spring Security
- Hibernate
- AI
- Python
- FastAPI
- LangGraph
- MCP
- pgvector
- OpenAI
- Frontend
- React
- TypeScript
- Redux
- Ant Design
- Database
- PostgreSQL
- pgvector
- Payments
- Stripe
Working with VectorLink Labs has been a great experience. They helped us turn a complex idea into a product our customers genuinely love to use. The project continues to grow, and their team has been responsive, reliable, and committed every step of the way. We look forward to building even more together.
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